Andy H.F. Chow

Dynamic system optimal traffic assignment – a state-dependent control theoretic approach

This study investigates analytical dynamic system optimal assignment with departure time choice in a rigorous and original way. Dynamic system optimal assignment is formulated here as a state-dependent optimal control problem. A fixed volume of traffic is assigned to departure times and routes such that the total system travel cost is minimised. Solution algorithms are presented and the effect of time discretisation on the quality of calculated assignments is discussed. Calculating dynamic system optimal assignment and the associated optimal toll is shown to be difficult for practical implementation. We therefore consider some practical tolling strategies for dynamic management of network traffic. The tolling strategies considered include uniform and congestion-based tolling strategies. This study contributes to the literature on dynamic traffic modelling and management, and to support further analysis and model development in this area.

Modelling urban traffic dynamics based upon the variational formulation of kinematic waves

This paper presents a dynamic traffic modelling framework based on the variational formulation of kinematic waves. We compare the effectiveness of this relatively recent numerical method with the traditional Godunov-based cell transmission method on various aspects including modelling shocks, dispersion of vehicle platoons, moving bottlenecks, and traffic characteristics with respect to real-world observations made in Central London, UK. The results suggest that the variational method is able to produce high-quality estimates both theoretically and empirically. This study opens up a new research direction in the area of urban traffic modelling and optimisation.

Identification of critical combination of vulnerable links in transportation networks – a global optimisation approach

This paper presents a global optimisation framework for identifying the most critical combination of vulnerable links in a transportation network. The problem is formulated as a mixed-integer non-linear programme with equilibrium constraints, aiming to determine the combination of links whose deterioration would induce the most increase in total travel cost in the network. A global optimisation solution method applying a piecewise linearisation approach and range-reduction technique is developed to solve the model. From the numerical results, it is interesting and counterintuitive to note that the set of most vulnerable links when simultaneous multiple-link failure occurs is not simply the combination of the most vulnerable links with single-link failure, and the links in the critical combination of vulnerable links are not necessarily connected or even in the neighbourhood of each other. The numerical results also show that the ranking of vulnerable links will be significantly affected by certain input parameters.

Robust Optimization of Dynamic Motorway Traffic via Ramp Metering

This paper presents a robust optimization model for motorway management. The optimization aims to minimize motorway delay via ramp metering with consideration of uncertainties in traffic demand and its characteristics. The robust optimization is formulated as a minimax problem and solved by a two-stage solution procedure. The performances of different control policies are illustrated through working examples with traffic data collected from the M25 motorway in the United Kingdom. Experiments reveal that the robust control provides reliable performance over a range of uncertain scenarios. Results also show that the proposed robust controller is particularly effective during transition periods when congestion has not yet fully developed.

METANET model improvement for traffic control

The METANET model, deduced based on equilibrium state assumption, provides a candidate model for freeway traffic control design since it has both speed and density dynamics, but the previous work parameterized the speed control variable to be highly nonlinear, which caused difficulty in control design and implementation. Besides, the model could not catch quick and significant changes in traffic dynamics. This paper suggests improvement on the dynamics model in two aspects: (a) to drop the nonlinear parameterization in the speed control variable for simplicity; and (b) to propose several alternatives for the convection term of the speed dynamics. Model calibration using Berkeley Highway Lab data and simulation for comparison are presented to show the effectiveness of the improvements.

Optimal Control of Motorways by Ramp Metering, Variable Speed Limits, and Hard-Shoulder Running

Ramp metering, variable speed limits, and hard-shoulder running have been used for managing motorway traffic congestion. The majority of the research in the area of ramp metering strategies is concentrated on local traffic responsive algorithms, such as ALINEA. Recently, researchers have studied the effects of variable speed limits and hard-shoulder running applied on motorways. This paper presents an optimization framework for all these control strategies. The framework was developed on the basis of a macroscopic cell transmission model (CTM), which described traffic dynamics through a piecewise linear fundamental diagram. With the piecewise linear nature of the CTM, the authors formulated optimal control problems that would seek the optimal control policies for minimizing total delays on motorways. The optimal control problems were formulated as linear programming or mixed integer linear programming and were solved by using the IBM CPLEX solver. The performances of different control strategies were tested on real scenarios on the M25 motorway in England, where significant improvements were observed with proper implementation.

Modeling and optimization of road transport facility operations

Purpose – This paper aims to present a linear mathematical framework for modeling and optimizing road transport infrastructure. The framework assesses and optimizes performance of existing transport facility rather than relying on building new roads for the ever-increasing travel demand. Design/methodology/approach – The mathematical framework is built upon a traffic model called Cell Transmission Model (CTM). CTM describes the relationship and evolution of traffic flow and concentration over space and time. The model is parsimonious and accurate in predicting traffic dynamics. More importantly, the traffic flow model is piecewise linear with which the corresponding transport facility optimization problem can be formulated as a Linear Programming (LP) problem and solved by established solution algorithm for global optimality. Findings – We select a section on England Motorway M25 as a case study. With traffic data, we first calibrate the CTM, and we are able to produce traffic estimation with a reasonable...

Modeling and Data Fusion of Dynamic Highway Traffic

This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected from the California I-880 corridor in the San Francisco Bay Area from the Mobile Century experiment. Results revealed that the proposed fusion algorithm can work with data sources that are different in their spatiotemporal granularity and improve the accuracy of state estimation through incorporating multiple data sources. The present work contributed to the field of traffic engineering and management with the application of big data analytics.

Linear complementarity system approach to macroscopic freeway traffic modelling: uniqueness and convexity

The modified cell transmission model (MCTM) is formulated as a linear complementarity system (LCS) in this paper. The LCS formulation presented here consists of a discrete time linear system and a set of complementarity conditions. The discrete time linear system corresponds to the flow conservation equations while the complementarity conditions govern the sending and receiving functions defined by a series of ‘min’ operations in the MCTM. Technical difficulties encountered in application of the CTM and its extensions such as the hard nonlinearity caused by the ‘min’ operator can be avoided by the proposed LCS model. Several basic properties of the proposed LCS formulation, for example, existence and uniqueness of solution, are analysed based on the theory of linear complementarity problem. By this formulation, the theory of LCS developed in control and mathematical programming communities can be applied to the qualitative analysis of the CTM/MCTM. It is shown that the CTM/MCTM is equivalent to a convex programme which can be converted into a constrained linear quadratic control problem. It is found that these results are irrelevant to the cell partition, that is, different cell partitions will not change the uniqueness and convexity of solution. This property is essential for stability analysis and control synthesis. The proposed LCS formulation makes the CTM/MCTM convenient for the design of traffic state estimators, ramp metering controllers.

Heterogeneous urban traffic data and their integration through kernel-based interpolation

Purpose This paper aims to present collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach for assessing performance of urban transport network facilities. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. This paper contributes to analysis and management of urban transport facilities. Design/methodology/approach In this paper, the data fusion algorithm are developed by using a kernel-based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space-time resolution, with different level of accuracy and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. Findings The results show that the proposed algorithm is able to reconstruct accurately the underlying traffic flow pattern on transport network facilities with ordinary data sources on both virtual and real-world test beds. The data sources considered herein include loop detectors, cameras and GPS devices. The proposed data fusion algorithm does not require assumption and calibration of any underlying model. It is easy to implement and compute through advanced technique such as parallel computing. Originality/value The presented study is among the first utilizing and integrating heterogeneous urban traffic data from a major city like London. Unlike many other existing studies, the proposed method is data driven and does not require any assumption of underlying model. The formulation of the data fusion algorithm also allows it to be parallelized for large-scale applications. The study contributes to the application of Big Data analytics to infrastructure management.